{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.load('convergence_compare.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iteration_lstm = data['iteration_lstm']\n",
    "accuracy_lstm = data['accuracy_lstm']\n",
    "accuracy_a = data['accuracy_a']\n",
    "iteration_cnn = data['iteration_cnn']\n",
    "accuracy_bi = data['accuracy_bi']\n",
    "iteration_bi = data['iteration_bi']\n",
    "accuracy_cnn = data['accuracy_cnn']\n",
    "iteration_a = data['iteration_a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6t8xnmdSB5m50EWE0ujXH6Bpmznu+A5GlpaL+/TfUf/yGau8eFPHFJ7EkN3f0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f9e8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(5,4.0))\n",
    "stride = max( int(len(accuracy_a) / 8), 1)\n",
    "\n",
    "stride2 = max( int(len(accuracy_a) / 20), 1)\n",
    "\n",
    "\n",
    "iteration_bi2 = np.array([0.0, 0.01]+ iteration_bi.tolist())\n",
    "accuracy_bi2 = np.array([0.3,0.4]+ accuracy_bi.tolist())\n",
    "\n",
    "plt.plot(iteration_a*14000, accuracy_a, '-', label='LEAM', marker= 's', color='k', markevery=stride,lw=2,  mec='k', mew=1 , markersize=10)\n",
    "plt.plot(iteration_cnn[:70]*14000, accuracy_cnn[:70],'-', label='CNN', marker= 'o', color='r', markevery=stride,lw=2,  mec='k', mew=1 , markersize=10)\n",
    "plt.plot(iteration_lstm[:21]*14000, accuracy_lstm[:21],'-', label='LSTM', marker= 'v', color='orange', markevery=stride2,lw=2,  mec='k', mew=1 , markersize=10)\n",
    "plt.plot(iteration_bi2[:27]*14000, accuracy_bi2[:27], '-',label='Bi-Blosa', marker= 'p', color='g', markevery=stride2,lw=2,  mec='k', mew=1 , markersize=10)\n",
    "\n",
    "\n",
    "leg = plt.legend(fontsize=22, shadow=True, loc=(0.5, -0.0))\n",
    "plt.grid('on')\n",
    "\n",
    "plt.xlabel('# Iteration', fontsize=16)\n",
    "plt.ylabel('Accuracy (%)', fontsize=16)\n",
    "\n",
    "ax = plt.gca() \n",
    "ax.yaxis.set_label_coords(-0.09, 0.5) \n",
    "ax.xaxis.set_label_coords(0.48, -0.11) \n",
    "# X-axis label\n",
    "plt.xticks( (0.0, 0.1*14000, 0.2*14000), ('0', '2K','4K'), color='k', size=14)\n",
    "\n",
    "# Left Y-axis labels\n",
    "plt.yticks((0.50, 0.6, 0.70, 0.80), ('50', '60','70','80'), color='k', size=14)\n",
    "\n",
    "plt.xlim(0,4000)\n",
    "plt.ylim(0.45,0.80)\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "fig.savefig('yahoo_iteration.pdf', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00714286,  0.01428571,  0.02142857,  0.02857143,  0.03571429,\n",
       "        0.04285714,  0.05      ,  0.05714286,  0.06428571,  0.07142857,\n",
       "        0.07857143,  0.08571429,  0.09285714,  0.1       ,  0.10714286,\n",
       "        0.12142857,  0.12857143,  0.13571429,  0.15714286,  0.16428571,\n",
       "        0.17142857,  0.17857143,  0.2       ,  0.20714286,  0.22142857,\n",
       "        0.22857143,  0.24285714,  0.25714286,  0.26428571,  0.28571429,\n",
       "        0.29285714,  0.30714286,  0.32857143,  0.33571429,  0.35      ,\n",
       "        0.36428571,  0.37857143,  0.39285714,  0.42857143,  0.44285714,\n",
       "        0.45      ,  0.47142857,  0.49285714,  0.5       ,  0.58571429,\n",
       "        0.60714286,  0.61428571,  0.67142857,  0.72857143,  0.74285714,\n",
       "        0.77142857,  0.89285714,  0.94285714,  1.04285714])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iteration_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.    ,  0.6298,  0.7018,  0.715 ,  0.7209,  0.7253,  0.7265,\n",
       "        0.7265,  0.727 ,  0.7361,  0.7361,  0.7412,  0.7412,  0.744 ,\n",
       "        0.744 ,  0.744 ,  0.744 ,  0.745 ,  0.747 ,  0.7472,  0.7477,\n",
       "        0.749 ,  0.749 ,  0.7504,  0.7504,  0.7504,  0.7507,  0.7507])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
